Bibliographic record
Abstract
OBJECTIVES: To review current knowledge and recent concepts of the causes of injuries after minor impact automobile collisions and to acquaint those who treat these types of injuries with possible injury thresholds and mechanisms that may contribute to symptoms. DATA SOURCES: A review of literature involving mechanisms of injury, tissue tensile threshold, and neurologic considerations was undertaken. A hand-search of relevant engineering, medical/chiropractic, and computer Index Medicus sources in disciplines that cover the variety of symptoms was gathered. RESULTS: Soft-tissue injuries are difficult to diagnose or quantify. There is not one specific injury mechanism or threshold of injury. With physical variations of tissue tensile strength, anatomic differences, and neurophysiologic considerations, such threshold designation is not possible. CONCLUSIONS: To make a competent assessment of injury, it is important to evaluate each patient individually. The same collision may cause injury to some individuals and leave others unaffected. With the variability of human postures, tensile strength of the ligaments between individuals, body positions in the vehicle, collagen fibers in the same specimen segment, the amount of muscle activation and inhibition of muscles, the size of the spinal canals, and the excitability of the nervous system, one specific threshold is not possible. How individuals react to a stimulus varies widely, and it is evident peripheral stimulation has effects on the central nervous system. It is also clear that the somatosensory system of the neck, in addition to signaling nociception, may influence the control of neck, eyes, limbs, respiratory muscles, and some preganglionic sympathetic nerves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".